{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import gzip\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import sentencepiece as spm\n",
    "\n",
    "from representjs import RUN_DIR, CSNJS_DIR\n",
    "from data.precomputed_dataset import PrecomputedDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import textdistance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEFAULT_CSNJS_TRAIN_FILEPATH = str(CSNJS_DIR / \"javascript_dedupe_definitions_nonoverlap_v2_train.jsonl.gz\")\n",
    "DEFAULT_SPM_UNIGRAM_FILEPATH = str(CSNJS_DIR / \"csnjs_8k_9995p_unigram_url.model\")\n",
    "\n",
    "AUGMENTED_DATASET_PATH = \"/data/ajay/coderep/representjs/data/codesearchnet_javascript/javascript_augmented.pickle.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-11-14 01:54:04.871 | DEBUG    | data.precomputed_dataset:__init__:37 - Loading /data/ajay/coderep/representjs/data/codesearchnet_javascript/javascript_augmented.pickle.gz\n",
      "2020-11-14 01:56:14.477 | DEBUG    | data.precomputed_dataset:__init__:45 - Loaded 1843099 examples in 129.605s\n",
      "2020-11-14 01:56:24.691 | DEBUG    | data.precomputed_dataset:__init__:50 - Converted examples to lists of alternatives\n",
      "2020-11-14 01:56:25.034 | DEBUG    | data.precomputed_dataset:__init__:53 - Filtered dataset to 1644353 examples with at least 2 alternatives\n"
     ]
    }
   ],
   "source": [
    "config = dict(\n",
    "    train_filepath=AUGMENTED_DATASET_PATH,\n",
    "    program_mode=\"contrastive\",\n",
    "    limit_dataset_size=-1,\n",
    "    subword_regularization_alpha=0.1,\n",
    "    # MIN ALTERNATIVES = 2, SWREG=0.1 used for BiLSTM\n",
    "    # MIN ALT=1, SWREG=0.1 for contrastive Transformer  TODO: try this.\n",
    "    # MIN ALT=1, SWREG=0. for hybrid MLM, contrastive Transformer\n",
    "    min_alternatives=2,\n",
    "    max_length=1024,\n",
    "    spm_filepath=DEFAULT_SPM_UNIGRAM_FILEPATH\n",
    ")\n",
    "\n",
    "sp = spm.SentencePieceProcessor()\n",
    "sp.Load(config[\"spm_filepath\"])\n",
    "pad_id = sp.PieceToId(\"[PAD]\")\n",
    "mask_id = sp.PieceToId(\"[MASK]\")\n",
    "\n",
    "train_dataset = PrecomputedDataset(\n",
    "    config[\"train_filepath\"],\n",
    "    min_alternatives=config[\"min_alternatives\"],\n",
    "    program_mode=config[\"program_mode\"],\n",
    "    limit_size=config[\"limit_dataset_size\"],\n",
    "    sp=sp,\n",
    "    subword_regularization_alpha=config[\"subword_regularization_alpha\"],\n",
    "    max_length=config[\"max_length\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define edit distance dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EDDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, precomputed_dataset, limit_size=-1):\n",
    "        super().__init__()\n",
    "        self.precomputed_dataset = precomputed_dataset\n",
    "        self.limit_size = limit_size\n",
    "\n",
    "    def __len__(self):\n",
    "        if self.limit_size > 0:\n",
    "            return min(self.limit_size, len(self.precomputed_dataset))\n",
    "        return len(self.precomputed_dataset)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        a, b = self.precomputed_dataset[idx]\n",
    "        a = list(a.numpy())\n",
    "        b = list(b.numpy())\n",
    "        distance = textdistance.levenshtein.distance(a, b)\n",
    "\n",
    "        maxlen = max(len(a), len(b))\n",
    "        ratio = (maxlen - distance) / float(maxlen)  # similarity ratio\n",
    "        return ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "eddataset = EDDataset(train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define parallel dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = torch.utils.data.DataLoader(eddataset, batch_size=32, shuffle=False, num_workers=64, drop_last=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ratios = np.zeros(len(eddataset), dtype=np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text distance, similarities\n",
      "Processed 320 pairs of programs (0.01946054162336189 %). Average ratio: 0.3162925668898958 += 0.005729668634869503\n",
      "Processed 640 pairs of programs (0.03892108324672378 %). Average ratio: 0.3289400737521276 += 0.004047431244050049\n",
      "Processed 960 pairs of programs (0.05838162487008568 %). Average ratio: 0.32592086926750574 += 0.003194651072201106\n",
      "Processed 1280 pairs of programs (0.07784216649344756 %). Average ratio: 0.3283771853342653 += 0.0028183710123059957\n",
      "Processed 1600 pairs of programs (0.09730270811680947 %). Average ratio: 0.32819730119914103 += 0.0025136647565337325\n",
      "Processed 1920 pairs of programs (0.11676324974017135 %). Average ratio: 0.32767478017979657 += 0.002260463952219395\n",
      "Processed 2240 pairs of programs (0.13622379136353327 %). Average ratio: 0.32753357682536194 += 0.002072603762363024\n",
      "Processed 2560 pairs of programs (0.15568433298689513 %). Average ratio: 0.33590366400497346 += 0.0020475848607816147\n",
      "Processed 2880 pairs of programs (0.17514487461025705 %). Average ratio: 0.3395684464850697 += 0.0019632130883883445\n",
      "Processed 3200 pairs of programs (0.19460541623361893 %). Average ratio: 0.3420041150904572 += 0.0018901361400186355\n",
      "Processed 3520 pairs of programs (0.21406595785698085 %). Average ratio: 0.3466812335297798 += 0.0018449531243081325\n",
      "Processed 3840 pairs of programs (0.2335264994803427 %). Average ratio: 0.34440114165931085 += 0.0017576342099954174\n",
      "Processed 4160 pairs of programs (0.2529870411037046 %). Average ratio: 0.34303078410294735 += 0.0016862054360772345\n",
      "Processed 4480 pairs of programs (0.27244758272706654 %). Average ratio: 0.343154686849584 += 0.0016371679828215719\n",
      "Processed 4800 pairs of programs (0.2919081243504284 %). Average ratio: 0.3432262542196852 += 0.0015912975607557571\n",
      "Processed 5120 pairs of programs (0.31136866597379026 %). Average ratio: 0.34469479690990895 += 0.0015430642727528527\n",
      "Processed 5440 pairs of programs (0.33082920759715223 %). Average ratio: 0.34630166691228126 += 0.0015084076215718888\n",
      "Processed 5760 pairs of programs (0.3502897492205141 %). Average ratio: 0.3458062915785586 += 0.0014633827496180196\n",
      "Processed 6080 pairs of programs (0.36975029084387595 %). Average ratio: 0.3456518111552592 += 0.0014219975940546771\n",
      "Processed 6400 pairs of programs (0.38921083246723787 %). Average ratio: 0.34529850588156535 += 0.0013811130376758895\n",
      "Processed 6720 pairs of programs (0.4086713740905998 %). Average ratio: 0.34506671330210553 += 0.0013380799818282722\n",
      "Processed 7040 pairs of programs (0.4281319157139617 %). Average ratio: 0.3456073712062669 += 0.0012958008509576409\n",
      "Processed 7360 pairs of programs (0.4475924573373235 %). Average ratio: 0.34576719073344736 += 0.0012617272972220955\n",
      "Processed 7680 pairs of programs (0.4670529989606854 %). Average ratio: 0.345404216957765 += 0.001236044335766132\n",
      "Processed 8000 pairs of programs (0.4865135405840474 %). Average ratio: 0.3447389432977965 += 0.0012103638904015048\n",
      "Processed 8320 pairs of programs (0.5059740822074092 %). Average ratio: 0.34429710597857993 += 0.001185132564024076\n",
      "Processed 8640 pairs of programs (0.5254346238307711 %). Average ratio: 0.3435458658606521 += 0.0011608155901168087\n",
      "Processed 8960 pairs of programs (0.5448951654541331 %). Average ratio: 0.3426764317755199 += 0.0011409655521508028\n",
      "Processed 9280 pairs of programs (0.5643557070774949 %). Average ratio: 0.3423726285052173 += 0.0011216020106161592\n",
      "Processed 9600 pairs of programs (0.5838162487008568 %). Average ratio: 0.34222433935185487 += 0.0011050258680220474\n",
      "Processed 9920 pairs of programs (0.6032767903242188 %). Average ratio: 0.3417091056512304 += 0.0010849033812743018\n",
      "Processed 10240 pairs of programs (0.6227373319475805 %). Average ratio: 0.3417957307754748 += 0.0010676169637699813\n",
      "Processed 17280 pairs of programs (1.0508692476615422 %). Average ratio: 0.3417228766017447 += 0.000820639939243561\n",
      "Processed 17600 pairs of programs (1.0703297892849042 %). Average ratio: 0.3418599475505246 += 0.0008126221144179318\n",
      "Processed 17920 pairs of programs (1.0897903309082662 %). Average ratio: 0.34156744384412746 += 0.0008041511121188576\n",
      "Processed 21760 pairs of programs (1.323316830388609 %). Average ratio: 0.3451135691969896 += 0.0007422899241121053\n",
      "Processed 22080 pairs of programs (1.3427773720119707 %). Average ratio: 0.34534372846638495 += 0.0007368547620014991\n",
      "Processed 22400 pairs of programs (1.3622379136353324 %). Average ratio: 0.34560275747407504 += 0.0007321293553147211\n",
      "Processed 22720 pairs of programs (1.3816984552586944 %). Average ratio: 0.3453258385563715 += 0.0007253098521629934\n",
      "Processed 23040 pairs of programs (1.4011589968820564 %). Average ratio: 0.34496254209057736 += 0.0007187811553630315\n",
      "Processed 23360 pairs of programs (1.4206195385054183 %). Average ratio: 0.3446088367074927 += 0.0007124295783177084\n",
      "Processed 23680 pairs of programs (1.44008008012878 %). Average ratio: 0.34455184439947917 += 0.000705251487914845\n",
      "Processed 24000 pairs of programs (1.459540621752142 %). Average ratio: 0.3444883769145719 += 0.000699781294824667\n",
      "Processed 24320 pairs of programs (1.4790011633755038 %). Average ratio: 0.3444524731896153 += 0.0006944953707510977\n",
      "Processed 24640 pairs of programs (1.4984617049988658 %). Average ratio: 0.34489733065871475 += 0.000690519441628676\n",
      "Processed 24960 pairs of programs (1.5179222466222277 %). Average ratio: 0.34525162611710164 += 0.0006871243026066044\n",
      "Processed 25280 pairs of programs (1.5373827882455897 %). Average ratio: 0.3451486010245422 += 0.0006818056672223165\n",
      "Processed 25600 pairs of programs (1.5568433298689515 %). Average ratio: 0.3450062131639759 += 0.0006779062085490412\n",
      "Processed 25920 pairs of programs (1.5763038714923134 %). Average ratio: 0.3450220432875339 += 0.0006736600381147658\n",
      "Processed 26240 pairs of programs (1.5957644131156752 %). Average ratio: 0.34497194340453297 += 0.0006696187354447965\n",
      "Processed 26560 pairs of programs (1.6152249547390374 %). Average ratio: 0.34461397889933676 += 0.0006653531448022797\n",
      "Processed 26880 pairs of programs (1.6346854963623991 %). Average ratio: 0.34430747927006605 += 0.000661169607271452\n",
      "Processed 27200 pairs of programs (1.6541460379857609 %). Average ratio: 0.34490766569299974 += 0.0006592289673575218\n",
      "Processed 27520 pairs of programs (1.6736065796091228 %). Average ratio: 0.3453331821878207 += 0.0006562195754192163\n",
      "Processed 27840 pairs of programs (1.6930671212324846 %). Average ratio: 0.34548726391377804 += 0.0006527994287785596\n",
      "Processed 28160 pairs of programs (1.7125276628558468 %). Average ratio: 0.34553939078104695 += 0.0006487309967720103\n",
      "Processed 28480 pairs of programs (1.7319882044792085 %). Average ratio: 0.3453878414922742 += 0.0006442633064573571\n",
      "Processed 28800 pairs of programs (1.7514487461025705 %). Average ratio: 0.34541290673665453 += 0.0006412908577772717\n",
      "Processed 29120 pairs of programs (1.7709092877259323 %). Average ratio: 0.3456711415324472 += 0.0006378136707327212\n",
      "Processed 29440 pairs of programs (1.790369829349294 %). Average ratio: 0.3461482936433027 += 0.0006345861900233065\n",
      "Processed 29760 pairs of programs (1.8098303709726562 %). Average ratio: 0.3463865799269553 += 0.0006307905234044746\n",
      "Processed 30080 pairs of programs (1.829290912596018 %). Average ratio: 0.34660431596499897 += 0.0006275111147011848\n",
      "Processed 30400 pairs of programs (1.84875145421938 %). Average ratio: 0.34696113685024577 += 0.0006244746941875519\n",
      "Processed 30720 pairs of programs (1.8682119958427417 %). Average ratio: 0.3473832343905163 += 0.0006220888957268909\n",
      "Processed 31040 pairs of programs (1.8876725374661034 %). Average ratio: 0.3474358117876847 += 0.0006186442045244735\n",
      "Processed 31360 pairs of programs (1.9071330790894656 %). Average ratio: 0.34708205271901166 += 0.0006147202087122667\n",
      "Processed 31680 pairs of programs (1.9265936207128274 %). Average ratio: 0.3472682832548404 += 0.0006124006915262766\n",
      "Processed 32000 pairs of programs (1.9460541623361896 %). Average ratio: 0.34736571560212526 += 0.0006094848066146744\n",
      "Processed 32320 pairs of programs (1.9655147039595513 %). Average ratio: 0.34715669141285915 += 0.0006055655959716767\n",
      "Processed 32640 pairs of programs (1.9849752455829133 %). Average ratio: 0.34709887817571344 += 0.0006016557418380497\n",
      "Processed 32960 pairs of programs (2.0044357872062752 %). Average ratio: 0.3469662405514028 += 0.0005983295613623186\n",
      "Processed 33280 pairs of programs (2.023896328829637 %). Average ratio: 0.3472002507563494 += 0.0005961631475358872\n",
      "Processed 33600 pairs of programs (2.0433568704529987 %). Average ratio: 0.34734689274560326 += 0.0005928824130808848\n",
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      "Processed 34240 pairs of programs (2.0822779536997227 %). Average ratio: 0.3471772514817351 += 0.0005869554525203555\n",
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      "Processed 34880 pairs of programs (2.121199036946446 %). Average ratio: 0.34715701277036354 += 0.0005827512544363866\n",
      "Processed 35200 pairs of programs (2.1406595785698084 %). Average ratio: 0.34752315656088906 += 0.0005816389777314728\n",
      "Processed 35520 pairs of programs (2.16012012019317 %). Average ratio: 0.3475030348703759 += 0.0005788028694219987\n",
      "Processed 35840 pairs of programs (2.1795806618165323 %). Average ratio: 0.347568145558894 += 0.0005757677857935259\n",
      "Processed 36160 pairs of programs (2.199041203439894 %). Average ratio: 0.34746113225533504 += 0.0005728046126706866\n",
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      "Processed 40640 pairs of programs (2.4714887861669603 %). Average ratio: 0.34711880431713726 += 0.0005408877324956516\n",
      "Processed 40960 pairs of programs (2.490949327790322 %). Average ratio: 0.3471017546491127 += 0.0005386989005213848\n",
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      "Processed 44800 pairs of programs (2.724475827270665 %). Average ratio: 0.34759425998196253 += 0.0005138360278495422\n",
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      "Processed 45760 pairs of programs (2.782857452140751 %). Average ratio: 0.3483843161938123 += 0.0005096548329831939\n",
      "Processed 46080 pairs of programs (2.8023179937641127 %). Average ratio: 0.3484183817222171 += 0.000507913697881663\n",
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      "Processed 47360 pairs of programs (2.88016016025756 %). Average ratio: 0.34854800833118055 += 0.0005016831706659234\n",
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      "Processed 48000 pairs of programs (2.919081243504284 %). Average ratio: 0.3479931756548698 += 0.0004983970097834564\n",
      "Processed 48320 pairs of programs (2.938541785127646 %). Average ratio: 0.3476938318888594 += 0.0004965349173872855\n",
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      "Processed 49280 pairs of programs (2.9969234099977315 %). Average ratio: 0.34690197792285254 += 0.0004916106748598752\n",
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      "Processed 1246400 pairs of programs (75.79880962299458 %). Average ratio: 0.346002867483293 += 9.998998362205418e-05\n",
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      "Processed 1281920 pairs of programs (77.95892974318774 %). Average ratio: 0.3458488685286946 += 9.851031195327372e-05\n",
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      "Processed 1286720 pairs of programs (78.25083786753817 %). Average ratio: 0.3458210780757165 += 9.831058168102216e-05\n",
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      "Processed 1287360 pairs of programs (78.28975895078489 %). Average ratio: 0.34581459300298867 += 9.828661591301779e-05\n",
      "Processed 1287680 pairs of programs (78.30921949240826 %). Average ratio: 0.3458136577900018 += 9.82744745033088e-05\n",
      "Processed 1288000 pairs of programs (78.32868003403162 %). Average ratio: 0.34581069726185754 += 9.826060266140271e-05\n",
      "Processed 1288320 pairs of programs (78.34814057565498 %). Average ratio: 0.34580768101990883 += 9.824696294831288e-05\n",
      "Processed 1288640 pairs of programs (78.36760111727834 %). Average ratio: 0.3458047160107196 += 9.82341090312631e-05\n",
      "Processed 1288960 pairs of programs (78.38706165890171 %). Average ratio: 0.3458063337233489 += 9.82198527536054e-05\n",
      "Processed 1289280 pairs of programs (78.40652220052507 %). Average ratio: 0.34580160361197265 += 9.820587423959991e-05\n",
      "Processed 1289600 pairs of programs (78.42598274214843 %). Average ratio: 0.34580045501956164 += 9.819103801860921e-05\n",
      "Processed 1289920 pairs of programs (78.4454432837718 %). Average ratio: 0.3457946108904917 += 9.817913116071694e-05\n",
      "Processed 1290240 pairs of programs (78.46490382539515 %). Average ratio: 0.34579369246946956 += 9.816503459085467e-05\n",
      "Processed 1290560 pairs of programs (78.48436436701853 %). Average ratio: 0.3457904355958777 += 9.815049115791282e-05\n",
      "Processed 1290880 pairs of programs (78.50382490864189 %). Average ratio: 0.3457918994624299 += 9.813708672067205e-05\n",
      "Processed 1291200 pairs of programs (78.52328545026525 %). Average ratio: 0.345788916547575 += 9.812303146823433e-05\n",
      "Processed 1291520 pairs of programs (78.54274599188861 %). Average ratio: 0.3457850452771946 += 9.810843523948728e-05\n",
      "Processed 1291840 pairs of programs (78.56220653351197 %). Average ratio: 0.3457828842969105 += 9.809360286352735e-05\n",
      "Processed 1292160 pairs of programs (78.58166707513533 %). Average ratio: 0.3457792967144241 += 9.80791104156784e-05\n",
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      "Processed 1293120 pairs of programs (78.64004870000542 %). Average ratio: 0.34577167604027825 += 9.803809537513218e-05\n",
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      "Processed 1293760 pairs of programs (78.67896978325214 %). Average ratio: 0.3457667593514055 += 9.801116519671858e-05\n",
      "Processed 1294080 pairs of programs (78.6984303248755 %). Average ratio: 0.34577219764749023 += 9.799875091756768e-05\n",
      "Processed 1294400 pairs of programs (78.71789086649886 %). Average ratio: 0.3457754132884348 += 9.798658210364993e-05\n",
      "Processed 1294720 pairs of programs (78.73735140812222 %). Average ratio: 0.34578264426102184 += 9.797861478117932e-05\n",
      "Processed 1295040 pairs of programs (78.75681194974558 %). Average ratio: 0.3457809318041507 += 9.796352697208359e-05\n",
      "Processed 1295360 pairs of programs (78.77627249136894 %). Average ratio: 0.3457801065506439 += 9.794842478843913e-05\n",
      "Processed 1295680 pairs of programs (78.79573303299232 %). Average ratio: 0.3457757092693488 += 9.793823498520422e-05\n",
      "Processed 1296000 pairs of programs (78.81519357461568 %). Average ratio: 0.3457733541496624 += 9.792816351742555e-05\n",
      "Processed 1296320 pairs of programs (78.83465411623904 %). Average ratio: 0.3457789412708234 += 9.791910349977815e-05\n",
      "Processed 1296640 pairs of programs (78.8541146578624 %). Average ratio: 0.34578519773100497 += 9.791247356560171e-05\n",
      "Processed 1296960 pairs of programs (78.87357519948576 %). Average ratio: 0.345778070952108 += 9.790000227661529e-05\n",
      "Processed 1297280 pairs of programs (78.89303574110912 %). Average ratio: 0.3457808764146129 += 9.788880119361165e-05\n",
      "Processed 1297600 pairs of programs (78.91249628273248 %). Average ratio: 0.3457763969067185 += 9.787586389165027e-05\n",
      "Processed 1297920 pairs of programs (78.93195682435584 %). Average ratio: 0.3457741335905155 += 9.78638143263379e-05\n",
      "Processed 1298240 pairs of programs (78.95141736597921 %). Average ratio: 0.34577036420141755 += 9.784993073410749e-05\n",
      "Processed 1298560 pairs of programs (78.97087790760257 %). Average ratio: 0.3457673162449383 += 9.783682908909645e-05\n",
      "Processed 1298880 pairs of programs (78.99033844922593 %). Average ratio: 0.34576319362690233 += 9.782569651378619e-05\n",
      "Processed 1299200 pairs of programs (79.00979899084929 %). Average ratio: 0.34576750804405215 += 9.78163042760108e-05\n",
      "Processed 1299520 pairs of programs (79.02925953247265 %). Average ratio: 0.3457658108956857 += 9.780311530836923e-05\n",
      "Processed 1299840 pairs of programs (79.04872007409601 %). Average ratio: 0.345762279168937 += 9.778992693304307e-05\n",
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      "Processed 1345600 pairs of programs (81.83157752623677 %). Average ratio: 0.34567211252224206 += 9.602925318796712e-05\n",
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      "Processed 1346560 pairs of programs (81.88995915110685 %). Average ratio: 0.3456668342983687 += 9.599076398685836e-05\n",
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      "Processed 1347200 pairs of programs (81.92888023435357 %). Average ratio: 0.34566173948376316 += 9.596676263565705e-05\n",
      "Processed 1347520 pairs of programs (81.94834077597693 %). Average ratio: 0.34565626080233486 += 9.59538801908872e-05\n",
      "Processed 1347840 pairs of programs (81.96780131760029 %). Average ratio: 0.34564960974277975 += 9.594115703385522e-05\n",
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      "Processed 1349120 pairs of programs (82.04564348409374 %). Average ratio: 0.3456515931746607 += 9.589735154977254e-05\n",
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      "Processed 1351040 pairs of programs (82.16240673383392 %). Average ratio: 0.345709721101412 += 9.585429513618155e-05\n",
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      "Processed 1401600 pairs of programs (85.23717231032509 %). Average ratio: 0.34556393387332884 += 9.410583018873401e-05\n",
      "Processed 1401920 pairs of programs (85.25663285194845 %). Average ratio: 0.34555934470902117 += 9.409371737730289e-05\n",
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      "Processed 1440960 pairs of programs (87.63081892999861 %). Average ratio: 0.3454077526243193 += 9.275461163994995e-05\n",
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      "Processed 1441600 pairs of programs (87.66974001324533 %). Average ratio: 0.3454001813621682 += 9.273122144252736e-05\n",
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      "Processed 1442880 pairs of programs (87.74758217973879 %). Average ratio: 0.3453932735615963 += 9.268345197428751e-05\n",
      "Processed 1443200 pairs of programs (87.76704272136215 %). Average ratio: 0.3453910847067695 += 9.267183409291973e-05\n",
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      "Processed 1443840 pairs of programs (87.80596380460887 %). Average ratio: 0.3453842599555809 += 9.2648615871581e-05\n",
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      "Processed 1446080 pairs of programs (87.9421875959724 %). Average ratio: 0.34540212926530284 += 9.257621729503898e-05\n",
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      "Processed 1447040 pairs of programs (88.00056922084248 %). Average ratio: 0.34540191207510207 += 9.254617521525889e-05\n",
      "Processed 1447360 pairs of programs (88.02002976246584 %). Average ratio: 0.34539975670509976 += 9.253420777256772e-05\n",
      "Processed 1447680 pairs of programs (88.03949030408921 %). Average ratio: 0.3453986334331215 += 9.252301688628838e-05\n",
      "Processed 1448000 pairs of programs (88.05895084571257 %). Average ratio: 0.3453974442942397 += 9.251308088003199e-05\n",
      "Processed 1448320 pairs of programs (88.07841138733593 %). Average ratio: 0.34539679076051105 += 9.250133869347455e-05\n",
      "Processed 1448640 pairs of programs (88.0978719289593 %). Average ratio: 0.3453958910308186 += 9.248928194281073e-05\n",
      "Processed 1448960 pairs of programs (88.11733247058265 %). Average ratio: 0.34539293593747816 += 9.247813283514365e-05\n",
      "Processed 1449280 pairs of programs (88.13679301220601 %). Average ratio: 0.3453906022756368 += 9.246674415614891e-05\n",
      "Processed 1449600 pairs of programs (88.15625355382937 %). Average ratio: 0.34538939936969826 += 9.24562537028362e-05\n",
      "Processed 1449920 pairs of programs (88.17571409545273 %). Average ratio: 0.34538993178298555 += 9.244455099543181e-05\n",
      "Processed 1450240 pairs of programs (88.19517463707611 %). Average ratio: 0.3453883626627857 += 9.243265787857108e-05\n",
      "Processed 1450560 pairs of programs (88.21463517869947 %). Average ratio: 0.345377970944102 += 9.242077113123668e-05\n",
      "Processed 1450880 pairs of programs (88.23409572032283 %). Average ratio: 0.3453767895342807 += 9.24106279373395e-05\n",
      "Processed 1451200 pairs of programs (88.25355626194619 %). Average ratio: 0.34537155467644154 += 9.23994407843298e-05\n",
      "Processed 1451520 pairs of programs (88.27301680356955 %). Average ratio: 0.34536842804575846 += 9.23879974998225e-05\n",
      "Processed 1451840 pairs of programs (88.2924773451929 %). Average ratio: 0.34536038414112635 += 9.2376739617544e-05\n",
      "Processed 1452160 pairs of programs (88.31193788681627 %). Average ratio: 0.345353820590364 += 9.236672081971925e-05\n",
      "Processed 1452480 pairs of programs (88.33139842843963 %). Average ratio: 0.34535087536149556 += 9.23563394003207e-05\n",
      "Processed 1452800 pairs of programs (88.350858970063 %). Average ratio: 0.34535116384013126 += 9.234690766657485e-05\n",
      "Processed 1453120 pairs of programs (88.37031951168636 %). Average ratio: 0.3453514589238652 += 9.233598929407644e-05\n",
      "Processed 1453440 pairs of programs (88.38978005330972 %). Average ratio: 0.3453466977651507 += 9.232966180947836e-05\n",
      "Processed 1453760 pairs of programs (88.40924059493308 %). Average ratio: 0.3453539072180756 += 9.232473014954493e-05\n",
      "Processed 1454080 pairs of programs (88.42870113655644 %). Average ratio: 0.34536077675001087 += 9.231603184898332e-05\n",
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      "Processed 1492160 pairs of programs (90.74450558973652 %). Average ratio: 0.34545780670516485 += 9.119471284139157e-05\n",
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      "Processed 1493120 pairs of programs (90.8028872146066 %). Average ratio: 0.3454576377094335 += 9.116423787308168e-05\n",
      "Processed 1493440 pairs of programs (90.82234775622996 %). Average ratio: 0.3454575375635995 += 9.115338063091469e-05\n",
      "Processed 1493760 pairs of programs (90.84180829785332 %). Average ratio: 0.3454575245866655 += 9.114285746219848e-05\n",
      "Processed 1494080 pairs of programs (90.86126883947668 %). Average ratio: 0.3454586900191854 += 9.113152345052482e-05\n",
      "Processed 1494400 pairs of programs (90.88072938110004 %). Average ratio: 0.3454573291991239 += 9.112030750695979e-05\n",
      "Processed 1494720 pairs of programs (90.90018992272341 %). Average ratio: 0.3454559905513778 += 9.111048148839164e-05\n",
      "Processed 1495040 pairs of programs (90.91965046434677 %). Average ratio: 0.34545417837322034 += 9.110164457687948e-05\n",
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      "Processed 1495680 pairs of programs (90.9585715475935 %). Average ratio: 0.3454552524250516 += 9.108182883515072e-05\n",
      "Processed 1496000 pairs of programs (90.97803208921685 %). Average ratio: 0.3454502378067928 += 9.10712637040925e-05\n",
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      "Processed 1496640 pairs of programs (91.01695317246357 %). Average ratio: 0.3454442463762075 += 9.104944619226603e-05\n",
      "Processed 1496960 pairs of programs (91.03641371408693 %). Average ratio: 0.34544271067295673 += 9.103771346707444e-05\n",
      "Processed 1497280 pairs of programs (91.05587425571031 %). Average ratio: 0.34543928679802804 += 9.102770606923848e-05\n",
      "Processed 1497600 pairs of programs (91.07533479733367 %). Average ratio: 0.34543639641696283 += 9.101705656502094e-05\n",
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      "Processed 1498880 pairs of programs (91.15317696382711 %). Average ratio: 0.34543857511879794 += 9.097717782466034e-05\n",
      "Processed 1499200 pairs of programs (91.17263750545047 %). Average ratio: 0.34544000751105935 += 9.09674143434881e-05\n",
      "Processed 1499520 pairs of programs (91.19209804707383 %). Average ratio: 0.34544300457698945 += 9.095873819989228e-05\n",
      "Processed 1499840 pairs of programs (91.2115585886972 %). Average ratio: 0.3454440158830528 += 9.094770737550735e-05\n",
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      "Processed 1500480 pairs of programs (91.25047967194392 %). Average ratio: 0.3454427537147815 += 9.092468822869093e-05\n",
      "Processed 1500800 pairs of programs (91.26994021356728 %). Average ratio: 0.3454437939843852 += 9.091447464818753e-05\n",
      "Processed 1501120 pairs of programs (91.28940075519064 %). Average ratio: 0.34544107251386963 += 9.090321944092646e-05\n",
      "Processed 1501440 pairs of programs (91.308861296814 %). Average ratio: 0.3454369289271924 += 9.089090632414062e-05\n",
      "Processed 1501760 pairs of programs (91.32832183843736 %). Average ratio: 0.34543225769266894 += 9.087935399562975e-05\n",
      "Processed 1502080 pairs of programs (91.34778238006072 %). Average ratio: 0.3454320254954453 += 9.086891861241899e-05\n",
      "Processed 1502400 pairs of programs (91.3672429216841 %). Average ratio: 0.34543094940978175 += 9.085880221814402e-05\n",
      "Processed 1502720 pairs of programs (91.38670346330746 %). Average ratio: 0.3454306918996597 += 9.084825085019085e-05\n",
      "Processed 1503040 pairs of programs (91.40616400493082 %). Average ratio: 0.3454291763304339 += 9.083913985578636e-05\n",
      "Processed 1503360 pairs of programs (91.42562454655418 %). Average ratio: 0.3454248693621383 += 9.08277391006567e-05\n",
      "Processed 1503680 pairs of programs (91.44508508817754 %). Average ratio: 0.3454160711189112 += 9.081533695872182e-05\n",
      "Processed 1504000 pairs of programs (91.4645456298009 %). Average ratio: 0.345412731986588 += 9.08049137887819e-05\n",
      "Processed 1504320 pairs of programs (91.48400617142426 %). Average ratio: 0.3454111774771474 += 9.079586850983766e-05\n",
      "Processed 1504640 pairs of programs (91.50346671304762 %). Average ratio: 0.34540769821989287 += 9.078508316298317e-05\n",
      "Processed 1504960 pairs of programs (91.52292725467099 %). Average ratio: 0.34540600764925045 += 9.077532650159522e-05\n",
      "Processed 1505280 pairs of programs (91.54238779629435 %). Average ratio: 0.3454037610555862 += 9.076532589073743e-05\n",
      "Processed 1505600 pairs of programs (91.56184833791771 %). Average ratio: 0.3454006702002474 += 9.075445154044691e-05\n",
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      "Processed 1539200 pairs of programs (93.6052052083707 %). Average ratio: 0.34552661440155924 += 8.980241103604752e-05\n",
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      "Processed 1543040 pairs of programs (93.83873170785105 %). Average ratio: 0.3455133715677269 += 8.967846197494684e-05\n",
      "Processed 1543360 pairs of programs (93.85819224947441 %). Average ratio: 0.3455108335559315 += 8.966782814987464e-05\n",
      "Processed 1543680 pairs of programs (93.87765279109777 %). Average ratio: 0.3455106245200852 += 8.965777264199656e-05\n",
      "Processed 1544000 pairs of programs (93.89711333272113 %). Average ratio: 0.3455100462559459 += 8.964806006880873e-05\n",
      "Processed 1544320 pairs of programs (93.9165738743445 %). Average ratio: 0.34551020993795156 += 8.963884285574472e-05\n",
      "Processed 1544640 pairs of programs (93.93603441596787 %). Average ratio: 0.34550715828948436 += 8.962831505820917e-05\n",
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      "Processed 1545280 pairs of programs (93.97495549921459 %). Average ratio: 0.34550062921052854 += 8.960815377654514e-05\n",
      "Processed 1545600 pairs of programs (93.99441604083795 %). Average ratio: 0.3454967887268229 += 8.95991323074543e-05\n",
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      "Processed 1546560 pairs of programs (94.05279766570803 %). Average ratio: 0.3454903036853625 += 8.95665643434382e-05\n",
      "Processed 1546880 pairs of programs (94.07225820733139 %). Average ratio: 0.34549060670255904 += 8.955630540764806e-05\n",
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      "Processed 1548800 pairs of programs (94.18902145707156 %). Average ratio: 0.34548410966133763 += 8.949611923022797e-05\n",
      "Processed 1549120 pairs of programs (94.20848199869492 %). Average ratio: 0.3454818462927421 += 8.948506480634513e-05\n",
      "Processed 1549440 pairs of programs (94.22794254031828 %). Average ratio: 0.3454821763136703 += 8.947556737788478e-05\n",
      "Processed 1549760 pairs of programs (94.24740308194166 %). Average ratio: 0.3454813907372006 += 8.946473916302789e-05\n",
      "Processed 1550080 pairs of programs (94.26686362356502 %). Average ratio: 0.3454793435708528 += 8.94557543764764e-05\n",
      "Processed 1550400 pairs of programs (94.28632416518838 %). Average ratio: 0.34548008733346885 += 8.94454707770384e-05\n",
      "Processed 1550720 pairs of programs (94.30578470681174 %). Average ratio: 0.3454787020856404 += 8.943489570202335e-05\n",
      "Processed 1551040 pairs of programs (94.3252452484351 %). Average ratio: 0.3454749552996073 += 8.942384485359824e-05\n",
      "Processed 1551360 pairs of programs (94.34470579005846 %). Average ratio: 0.3454701340152246 += 8.941319943752523e-05\n",
      "Processed 1551680 pairs of programs (94.36416633168182 %). Average ratio: 0.34546607120245193 += 8.94020931323449e-05\n",
      "Processed 1552000 pairs of programs (94.38362687330518 %). Average ratio: 0.34547200744831535 += 8.939367508729141e-05\n",
      "Processed 1552320 pairs of programs (94.40308741492855 %). Average ratio: 0.34546949467845317 += 8.938366798958236e-05\n",
      "Processed 1552640 pairs of programs (94.42254795655191 %). Average ratio: 0.34546706877643746 += 8.937291347204812e-05\n",
      "Processed 1552960 pairs of programs (94.44200849817527 %). Average ratio: 0.34546721454488066 += 8.936259495183268e-05\n",
      "Processed 1553280 pairs of programs (94.46146903979863 %). Average ratio: 0.3454676122403443 += 8.935064729847642e-05\n",
      "Processed 1553600 pairs of programs (94.48092958142199 %). Average ratio: 0.3454685550785536 += 8.934009971805016e-05\n",
      "Processed 1553920 pairs of programs (94.50039012304535 %). Average ratio: 0.345474854052755 += 8.933267904314435e-05\n",
      "Processed 1554240 pairs of programs (94.51985066466871 %). Average ratio: 0.345477333875641 += 8.932245670905229e-05\n",
      "Processed 1554560 pairs of programs (94.53931120629207 %). Average ratio: 0.3454795569905428 += 8.931255597998396e-05\n",
      "Processed 1554880 pairs of programs (94.55877174791544 %). Average ratio: 0.34548068469062065 += 8.930090231320461e-05\n",
      "Processed 1555200 pairs of programs (94.5782322895388 %). Average ratio: 0.3454810704305418 += 8.928877131164968e-05\n",
      "Processed 1555520 pairs of programs (94.59769283116216 %). Average ratio: 0.34548549618355706 += 8.927884414146309e-05\n",
      "Processed 1555840 pairs of programs (94.61715337278552 %). Average ratio: 0.3454855029403092 += 8.926820582386811e-05\n",
      "Processed 1556160 pairs of programs (94.63661391440888 %). Average ratio: 0.345484427665735 += 8.925628321802354e-05\n",
      "Processed 1556480 pairs of programs (94.65607445603224 %). Average ratio: 0.34548028755314547 += 8.924593559900343e-05\n",
      "Processed 1556800 pairs of programs (94.6755349976556 %). Average ratio: 0.34548156933123886 += 8.923619401453805e-05\n",
      "Processed 1557120 pairs of programs (94.69499553927896 %). Average ratio: 0.3454796295936495 += 8.922483286371678e-05\n",
      "Processed 1557440 pairs of programs (94.71445608090234 %). Average ratio: 0.3454765398578868 += 8.921378176847087e-05\n",
      "Processed 1557760 pairs of programs (94.7339166225257 %). Average ratio: 0.3454719528639084 += 8.920348588540328e-05\n",
      "Processed 1558080 pairs of programs (94.75337716414906 %). Average ratio: 0.345467264847532 += 8.91929191988298e-05\n",
      "Processed 1558400 pairs of programs (94.77283770577242 %). Average ratio: 0.34546445014599314 += 8.91826830427066e-05\n",
      "Processed 1558720 pairs of programs (94.79229824739578 %). Average ratio: 0.3454614228054795 += 8.917251061026343e-05\n",
      "Processed 1559040 pairs of programs (94.81175878901915 %). Average ratio: 0.34546017964041337 += 8.916208982273867e-05\n",
      "Processed 1559360 pairs of programs (94.83121933064251 %). Average ratio: 0.3454683862271839 += 8.915514857744968e-05\n",
      "Processed 1559680 pairs of programs (94.85067987226587 %). Average ratio: 0.3454757825409689 += 8.91475079551055e-05\n",
      "Processed 1560000 pairs of programs (94.87014041388923 %). Average ratio: 0.3454808352756277 += 8.913967695885706e-05\n",
      "Processed 1560320 pairs of programs (94.8896009555126 %). Average ratio: 0.3454771324738824 += 8.912730605857367e-05\n",
      "Processed 1560640 pairs of programs (94.90906149713597 %). Average ratio: 0.34548237335067594 += 8.911956538675557e-05\n",
      "Processed 1560960 pairs of programs (94.92852203875933 %). Average ratio: 0.34548148093216485 += 8.911011683564292e-05\n",
      "Processed 1561280 pairs of programs (94.94798258038269 %). Average ratio: 0.345491667790281 += 8.910518308665513e-05\n",
      "Processed 1561600 pairs of programs (94.96744312200605 %). Average ratio: 0.3454934212289641 += 8.909636819502817e-05\n",
      "Processed 1561920 pairs of programs (94.9869036636294 %). Average ratio: 0.34549454142677377 += 8.908596788576884e-05\n",
      "Processed 1562240 pairs of programs (95.00636420525277 %). Average ratio: 0.34549157928345214 += 8.907558738987372e-05\n",
      "Processed 1562560 pairs of programs (95.02582474687613 %). Average ratio: 0.3454889627109639 += 8.906475089317854e-05\n",
      "Processed 1562880 pairs of programs (95.04528528849949 %). Average ratio: 0.3454866670857401 += 8.905402661157504e-05\n",
      "Processed 1563200 pairs of programs (95.06474583012286 %). Average ratio: 0.34548531466298044 += 8.904311335338158e-05\n",
      "Processed 1563520 pairs of programs (95.08420637174622 %). Average ratio: 0.345482950205358 += 8.903202612229351e-05\n",
      "Processed 1563840 pairs of programs (95.10366691336958 %). Average ratio: 0.3454830030965781 += 8.902135535064592e-05\n",
      "Processed 1564160 pairs of programs (95.12312745499294 %). Average ratio: 0.3454799920526341 += 8.901111009320711e-05\n",
      "Processed 1564480 pairs of programs (95.1425879966163 %). Average ratio: 0.34547935639230315 += 8.90008929679647e-05\n",
      "Processed 1564800 pairs of programs (95.16204853823966 %). Average ratio: 0.34547847959262223 += 8.898924408289457e-05\n",
      "Processed 1565120 pairs of programs (95.18150907986302 %). Average ratio: 0.345474342734828 += 8.897867175711163e-05\n",
      "Processed 1565440 pairs of programs (95.2009696214864 %). Average ratio: 0.34546846840406703 += 8.897019612721088e-05\n",
      "Processed 1565760 pairs of programs (95.22043016310975 %). Average ratio: 0.34546429979359244 += 8.8962230613449e-05\n",
      "Processed 1566080 pairs of programs (95.23989070473311 %). Average ratio: 0.3454627781060155 += 8.895161247525012e-05\n",
      "Processed 1566400 pairs of programs (95.25935124635647 %). Average ratio: 0.3454612310304651 += 8.89415344575524e-05\n",
      "Processed 1566720 pairs of programs (95.27881178797983 %). Average ratio: 0.34545969242246577 += 8.893096772277414e-05\n",
      "Processed 1567040 pairs of programs (95.2982723296032 %). Average ratio: 0.3454578550379601 += 8.892007319869328e-05\n",
      "Processed 1567360 pairs of programs (95.31773287122655 %). Average ratio: 0.34545711044765637 += 8.891076216388096e-05\n",
      "Processed 1567680 pairs of programs (95.33719341284991 %). Average ratio: 0.3454540589132145 += 8.890093899441519e-05\n",
      "Processed 1568000 pairs of programs (95.35665395447327 %). Average ratio: 0.345451643520238 += 8.889024282989008e-05\n",
      "Processed 1568320 pairs of programs (95.37611449609665 %). Average ratio: 0.34545062274105465 += 8.888005382515278e-05\n",
      "Processed 1568640 pairs of programs (95.39557503772001 %). Average ratio: 0.34544769114590157 += 8.886992539678944e-05\n",
      "Processed 1568960 pairs of programs (95.41503557934337 %). Average ratio: 0.3454490707215653 += 8.886067655800977e-05\n",
      "Processed 1569280 pairs of programs (95.43449612096673 %). Average ratio: 0.34544650442625136 += 8.885079540227823e-05\n",
      "Processed 1569600 pairs of programs (95.45395666259009 %). Average ratio: 0.34544887362739374 += 8.884206276658514e-05\n",
      "Processed 1569920 pairs of programs (95.47341720421345 %). Average ratio: 0.34544829245564274 += 8.88317937839585e-05\n",
      "Processed 1570240 pairs of programs (95.49287774583681 %). Average ratio: 0.3454460560013982 += 8.88207061042264e-05\n",
      "Processed 1570560 pairs of programs (95.51233828746018 %). Average ratio: 0.3454437235942223 += 8.881074040135798e-05\n",
      "Processed 1570880 pairs of programs (95.53179882908354 %). Average ratio: 0.3454419373623269 += 8.880054546262413e-05\n",
      "Processed 1571200 pairs of programs (95.5512593707069 %). Average ratio: 0.3454399340224973 += 8.879123466560288e-05\n",
      "Processed 1571520 pairs of programs (95.57071991233026 %). Average ratio: 0.3454384066741671 += 8.878059169042222e-05\n",
      "Processed 1571840 pairs of programs (95.59018045395362 %). Average ratio: 0.3454368207494252 += 8.877053886845694e-05\n",
      "Processed 1572160 pairs of programs (95.60964099557698 %). Average ratio: 0.3454342875974344 += 8.876065877189637e-05\n",
      "Processed 1572480 pairs of programs (95.62910153720034 %). Average ratio: 0.34543401247814604 += 8.87503549825425e-05\n",
      "Processed 1572800 pairs of programs (95.6485620788237 %). Average ratio: 0.34543152565133445 += 8.874170813928581e-05\n",
      "Processed 1573120 pairs of programs (95.66802262044706 %). Average ratio: 0.3454329433298938 += 8.87321635639603e-05\n",
      "Processed 1573440 pairs of programs (95.68748316207044 %). Average ratio: 0.34543215011499756 += 8.872271092271608e-05\n",
      "Processed 1573760 pairs of programs (95.7069437036938 %). Average ratio: 0.34543639714827684 += 8.871424427562762e-05\n",
      "Processed 1574080 pairs of programs (95.72640424531716 %). Average ratio: 0.34543413663516487 += 8.870440195276276e-05\n",
      "Processed 1574400 pairs of programs (95.74586478694052 %). Average ratio: 0.3454298905918121 += 8.869479714528422e-05\n",
      "Processed 1574720 pairs of programs (95.76532532856388 %). Average ratio: 0.3454280838213516 += 8.868407727542588e-05\n",
      "Processed 1575040 pairs of programs (95.78478587018724 %). Average ratio: 0.34542339600466887 += 8.867493002500705e-05\n",
      "Processed 1575360 pairs of programs (95.8042464118106 %). Average ratio: 0.34542169807601375 += 8.866498524196193e-05\n",
      "Processed 1575680 pairs of programs (95.82370695343397 %). Average ratio: 0.3454217372215362 += 8.865549406059715e-05\n",
      "Processed 1576000 pairs of programs (95.84316749505733 %). Average ratio: 0.3454187790307765 += 8.86447398504532e-05\n",
      "Processed 1576320 pairs of programs (95.86262803668069 %). Average ratio: 0.34541712327974816 += 8.86350047385289e-05\n",
      "Processed 1576640 pairs of programs (95.88208857830405 %). Average ratio: 0.3454164131402487 += 8.862500803660523e-05\n",
      "Processed 1576960 pairs of programs (95.90154911992741 %). Average ratio: 0.3454164084667903 += 8.861423053213613e-05\n",
      "Processed 1577280 pairs of programs (95.92100966155077 %). Average ratio: 0.345415279293675 += 8.8604381831402e-05\n",
      "Processed 1577600 pairs of programs (95.94047020317413 %). Average ratio: 0.34540950327439707 += 8.859387246226141e-05\n",
      "Processed 1577920 pairs of programs (95.95993074479749 %). Average ratio: 0.345406916641928 += 8.858294450964939e-05\n",
      "Processed 1578240 pairs of programs (95.97939128642085 %). Average ratio: 0.3454046202516836 += 8.857235591926442e-05\n",
      "Processed 1578560 pairs of programs (95.99885182804422 %). Average ratio: 0.34540923530160517 += 8.856323577969693e-05\n",
      "Processed 1578880 pairs of programs (96.01831236966758 %). Average ratio: 0.3454063354221637 += 8.855334169458478e-05\n",
      "Processed 1579200 pairs of programs (96.03777291129094 %). Average ratio: 0.3454015680235859 += 8.854341019411364e-05\n",
      "Processed 1579520 pairs of programs (96.0572334529143 %). Average ratio: 0.34540240579210946 += 8.853383258572674e-05\n",
      "Processed 1579840 pairs of programs (96.07669399453766 %). Average ratio: 0.3453985291465136 += 8.852364033819807e-05\n",
      "Processed 1580160 pairs of programs (96.09615453616102 %). Average ratio: 0.34539933469924594 += 8.851373859203525e-05\n",
      "Processed 1580480 pairs of programs (96.11561507778438 %). Average ratio: 0.3453987372955642 += 8.850321594518666e-05\n",
      "Processed 1580800 pairs of programs (96.13507561940776 %). Average ratio: 0.3453961459102411 += 8.849195652213155e-05\n",
      "Processed 1581120 pairs of programs (96.15453616103112 %). Average ratio: 0.34539713614250556 += 8.84821558135618e-05\n",
      "Processed 1581440 pairs of programs (96.17399670265448 %). Average ratio: 0.3453962600221523 += 8.847246521857821e-05\n",
      "Processed 1581760 pairs of programs (96.19345724427784 %). Average ratio: 0.34539393387005024 += 8.846135918931226e-05\n"
     ]
    }
   ],
   "source": [
    "print(\"text distance, similarities\")\n",
    "\n",
    "i = 0\n",
    "for batch in dl:\n",
    "    batch = batch.numpy()\n",
    "    all_ratios[i:i+len(batch)] = batch\n",
    "    i += len(batch)\n",
    "    if i % (32 * 10) == 0:\n",
    "        average_ratio = np.mean(all_ratios[:i])\n",
    "        sdev = np.std(all_ratios[:i])\n",
    "        serr = sdev / np.sqrt(i)\n",
    "        print(f\"Processed {i} pairs of programs ({i / len(all_ratios) * 100} %). Average ratio: {average_ratio} += {serr}\")\n",
    "\n",
    "average_ratio = np.mean(all_ratios[:i])\n",
    "sdev = np.std(all_ratios[:i])\n",
    "serr = sdev / np.sqrt(i)\n",
    "print(f\"Processed {i} pairs of programs. Average ratio: {average_ratio} += {serr}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 1644353 pairs of programs. Average ratio: 0.3452640314630394 += 8.669583973361393e-05\n"
     ]
    }
   ],
   "source": [
    "average_ratio = np.mean(all_ratios[:i])\n",
    "sdev = np.std(all_ratios[:i])\n",
    "serr = sdev / np.sqrt(i)\n",
    "print(f\"Processed {i} pairs of programs. Average ratio: {average_ratio} += {serr}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"positives_all_ratios.npy\", all_ratios)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_ratios = np.load(\"negatives_all_ratios_1641600.npy\")\n",
    "neg_ratios = np.random.choice(neg_ratios, len(all_ratios), replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1644353"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1644353"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(neg_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot()\n",
    "bins = np.arange(101)\n",
    "plt.hist(all_ratios * 100, bins=bins, alpha=0.5, density=True, label=\"Positives\", cumulative=False)\n",
    "plt.hist(neg_ratios * 100, bins=bins, alpha=0.5, density=True, label=\"Negatives\", cumulative=False)\n",
    "plt.xlim([0, 100])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Token similarity (%)\")\n",
    "# plt.show()\n",
    "plt.savefig(\"token_similarity.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(101)\n",
    "plt.hist(all_ratios * 100, bins=bins, histtype=\"step\", density=True, label=\"Positives\", cumulative=True)\n",
    "plt.hist(neg_ratios * 100, bins=bins, histtype=\"step\", density=True, label=\"Negatives\", cumulative=True)\n",
    "plt.xlim([0, 100])\n",
    "plt.ylim([0, 1])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Token similarity (%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean similarity, positives: 0.3452640314630394\n",
      "Mean similarity, negatives: 0.13737635457650332\n",
      "Median similarity, positives: 0.3389830508474576\n",
      "Median similarity, negatives: 0.134765625\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean similarity, positives:\", np.mean(all_ratios))\n",
    "print(\"Mean similarity, negatives:\", np.mean(neg_ratios))\n",
    "print(\"Median similarity, positives:\", np.median(all_ratios))\n",
    "print(\"Median similarity, negatives:\", np.median(neg_ratios))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(\"paper\")\n",
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "bins = np.arange(101)\n",
    "plt.hist(100 - all_ratios * 100, bins=bins, alpha=0.7, density=True, label=\"Positives\", cumulative=False)\n",
    "plt.hist(100 - neg_ratios * 100, bins=bins, alpha=0.7, density=True, label=\"Negatives\", cumulative=False)\n",
    "plt.xlim([0, 100])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Token difference (%)\")\n",
    "plt.ylabel(\"Fraction of pairs\")\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"token_difference_sns.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "bins = np.arange(101)\n",
    "plt.hist(100 - all_ratios * 100, bins=bins, alpha=0.7, density=True, label=\"Positives\", cumulative=False)\n",
    "plt.hist(100 - neg_ratios * 100, bins=bins, alpha=0.7, density=True, label=\"Negatives\", cumulative=False)\n",
    "plt.xlim([0, 100])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Token difference (%)\")\n",
    "plt.ylabel(\"Fraction of pairs\")\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"token_difference_sns_squeeze.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(101)\n",
    "plt.hist(100 - all_ratios * 100, bins=bins, histtype=\"step\", density=True, label=\"Positives\", cumulative=True)\n",
    "plt.hist(100 - neg_ratios * 100, bins=bins, histtype=\"step\", density=True, label=\"Negatives\", cumulative=True)\n",
    "plt.xlim([0, 100])\n",
    "plt.ylim([0, 1])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Token difference (%)\")\n",
    "plt.ylabel(\"Cumulative fraction of pairs\")\n",
    "plt.savefig(\"token_difference_cdf_sns.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZlbvXRNYZBNwAjHH3T8zsH9LVYBFJPY3Nj49kxul/HVjn7u8BmNljwESgJrLO5cBd7v4JgLt/mOqGikj6aGx+fCTTvdMP2BiZrg3nRR0LHGtmL5rZK2ZW0dyOzKzSzKrNrLqurq59LRYRkXZL1YPczsAgYBwwBbjXzHonruTuC9x9lLuPKikpSdGhRUQkWcmE/iagf2S6NJwXVQtUuXuDu/8deIfgl4CIiOSQZPr0VwCDzGwgQdhPBr6dsM5TBHf4D5hZX4LunvdS2VARkbSIllKGgiynHNVq6Lv7HjO7GlgKFAH3u/ubZjYHqHb3qnDZWWZWA+wFfuDuW9PZcBGRlCjwUsqJkqqy6e5LgCUJ826KfO/AjPBLRERylEori8RQdFw+aGx+nCj0RWJI4/LjS7V3RERiRKEvIhIjCn0RkRhR6IuIxIhCX0QkRhT6IiIxoiGbIjGhmvkCCn2R2NDYfAGFvojEUbTIWoEXWEuk0BeR+IlZkbUoPcgVEYkRhb6ISIwo9EVEYkShLyISIwp9EZEY0egdkQKmF7IkkUJfpIDphSxJpO4dEZEYUeiLiMSIQl9EJEYU+iIiMaIHuSISDzEushal0BeReIhxkbUohb5IAYmOyweNzZcvU+iLFBCNy5fW6EGuiEiMKPRFRGJEoS8iEiMKfRGRGFHoi4jEiEJfRCRGNGRTJM+pZr60hUJfJM9pbL60RVKhb2YVwC+BIuBX7n5LC+tdACwCyt29OmWtFBFpq2itHYh1vZ2oVkPfzIqAu4AzgVpghZlVuXtNwno9gGuBV9PRUBGRNlGtnWYl8yD368A6d3/P3XcDjwETm1nvJ8CtwM4Utk9ERFIomdDvB2yMTNeG85qY2Qigv7svPtiOzKzSzKrNrLqurq7NjRURkY7p8JBNM+sE/DtwfWvruvsCdx/l7qNKSko6emgREWmjZEJ/E9A/Ml0azmvUAxgKLDOz9cBooMrMRqWqkSIikhrJjN5ZAQwys4EEYT8Z+HbjQnevB/o2TpvZMuB/a/SOSPpobL60V6uh7+57zOxqYCnBkM373f1NM5sDVLt7VbobKSIH0th8aa+kxum7+xJgScK8m1pYd1zHmyUiIumg2jsiIjGi0BcRiRGFvohIjKjgmogUjmi9HdXaaZZCX0QKh+rttEqhL5IHouPyQWPzpf0U+iJ5QOPyJVX0IFdEJEYU+iIiMaLQFxGJEYW+iEiMKPRFRGJEo3dEcpTKJ0s6KPRFcpSGaUo6qHtHRCRGFPoiIjGi7h0RyW8qstYmCn0RyW8qstYm6t4REYkR3emL5BAN05R0U+iL5BAN05R0U/eOiEiMKPRFRGJEoS8iEiMKfRGRGNGDXJEs0mffSqYp9EWySKN1JNPUvSMiEiMKfRGRGFH3jojkl2iBNVCRtTZS6ItkmEotdJAKrHWIQl8kw/TwVrJJffoiIjGi0BcRiZGkQt/MKszsbTNbZ2Yzm1k+w8xqzGy1mf3BzAakvqkiItJRrYa+mRUBdwHjgROAKWZ2QsJqrwOj3H0YsAj4WaobKiIiHZfMg9yvA+vc/T0AM3sMmAjUNK7g7s9H1n8F+E4qGymS7zRiR3JFMqHfD9gYma4FTj7I+pcCTze3wMwqgUqAo4/W2FqJD43YkVyR0ge5ZvYdYBRwW3PL3X2Bu49y91ElJSWpPLSIiCQhmTv9TUD/yHRpOO8AZvZN4EfAP7r7rtQ0T0REUimZ0F8BDDKzgQRhPxn4dnQFMxsO3ANUuPuHKW+lSJ5RyWTJVa2GvrvvMbOrgaVAEXC/u79pZnOAanevIujOOQx4wswANrj7hDS2WySnqQ8/xaL1dlRrp0OSKsPg7kuAJQnzbop8/80Ut0tEZD/V20kZvZErIhIjCn0RkRhRlU2RFNELWJIPFPoiKaKHt5IP1L0jIhIjCn0RkRhR945IB6gfX/KNQl+kA9SPL/lG3TsiIjGiO32RNlBNnQxS6YW0UOiLtIG6czJIpRfSQt07IiIxotAXEYkRde+ItELDMqWQKPRFWqF+fCkk6t4REYkR3emLJNCwTClkCn2RBOrOyZLouHzQ2Pw0UeiLSG7QuPyMUOiLoBE6Eh8KfRHUpSPxodCX2NLdvcSRQl9iS3f3EkcKfYkNDcXMQaqkmXEKfYkN3dnnII3YyTiFvhQ09duLHEihLwVNd/ciB1LoS8HR3b1IyxT6kveae0Cru/scpoe3WaXQl7ynLpw8o4e3WaXQl7ykLhyR9lHoS95IDHrd3Yu0nUJfcpb66guESibnFIW+5BTdzRcg9eHnFIW+ZFziHXyUgr5AaIROzkoq9M2sAvglUAT8yt1vSVh+CPAwMBLYCkxy9/Wpbarkm5bCXcFegJrrwtHdfU5qNfTNrAi4CzgTqAVWmFmVu9dEVrsU+MTdv2Zmk4FbgUnpaLDkFt21x1ji3bxCPi8kc6f/dWCdu78HYGaPAROBaOhPBGaH3y8C7jQzc3dPYVulHQ4WyqmgYC8QiXfqyVDQ56VkQr8fsDEyXQuc3NI67r7HzOqBPsBH0ZXMrBKoDCd3mdnf2tPoAtSXhHOVL94H7IaU7jJvz0Ua5Pi5+BvMsEwdLMfPRUYd15GNM/og190XAAsAzKza3Udl8vi5SudiP52L/XQu9tO52M/Mqjuyfack1tkE9I9Ml4bzml3HzDoDvQge6IqISA5JJvRXAIPMbKCZdQUmA1UJ61QB/xJ+/8/AH9WfLyKSe1rt3gn76K8GlhIM2bzf3d80szlAtbtXAfcBj5jZOuBjgl8MrVnQgXYXGp2L/XQu9tO52E/nYr8OnQvTDbmISHwk070jIiIFQqEvIhIjWQl9M6sws7fNbJ2ZzcxGG7LBzPqb2fNmVmNmb5rZteH8r5jZ781sbfjfw7Pd1kwxsyIze93MfhdODzSzV8NrY2E4eKDgmVlvM1tkZmvM7C0zOyWu14WZXRf++/ibmf3azIrjdF2Y2f1m9mH0PaaWrgULzAvPy2ozG9Ha/jMe+pGyDuOBE4ApZnZCptuRJXuA6939BGA0cFX4s88E/uDug4A/hNNxcS3wVmT6VuB2d/8a8AlBiY84+CXwjLsPBk4iOCexuy7MrB9wDTDK3YcSDB5pLO0Sl+viQaAiYV5L18J4YFD4VQnc3drOs3Gn31TWwd13A41lHQqeu29x99fC7z8l+Ifdj+Dnfyhc7SHgvOy0MLPMrBQ4F/hVOG3A6QSlPCAm58LMegFjCUbB4e673X0bMb0uCEYVdgvf+ekObCFG14W7/4lgFGRUS9fCROBhD7wC9DazIw+2/2yEfnNlHfploR1ZZWbHAMOBV4Ej3H1LuOgD4IgsNSvTfgH8H2BfON0H2Obue8LpuFwbA4E64IGwq+tXZnYoMbwu3H0TMBfYQBD29cBK4nldRLV0LbQ5T/UgNwvM7DDgN8D33X17dFn4UlvBj6M1s28BH7r7ymy3JQd0BkYAd7v7cGAHCV05MbouDie4ex0IHAUcype7OmKto9dCNkI/mbIOBcvMuhAE/qPu/mQ4+78b/yQL//thttqXQWOACWa2nqCL73SCfu3e4Z/1EJ9roxaodfdXw+lFBL8E4nhdfBP4u7vXuXsD8CTBtRLH6yKqpWuhzXmajdBPpqxDQQr7rO8D3nL3f48sipax+BfgvzLdtkxz9xvcvdTdjyG4Bv7o7lOB5wlKeUB8zsUHwEYza6yeeAZB6fLYXRcE3Tqjzax7+O+l8VzE7rpI0NK1UAVMC0fxjAbqI91AzXP3jH8B5wDvAO8CP8pGG7L0c/8vgj/LVgOrwq9zCPqy/wCsBZ4DvpLttmb4vIwDfhd+/z+AvwDrgCeAQ7LdvgydgzKgOrw2ngIOj+t1AfxfYA3wN+AR4JA4XRfArwmeZzQQ/BV4aUvXAmAEoyHfBf5KMOrpoPtXGQYRkRjRg1wRkRhR6IuIxIhCX0QkRhT6IiIxotAXEYkRhb6khJn1MbNV4dcHZrYpMt01Yd31ZtY3jW0ZF6naOaGxkquZlYSVGl83s1PN7MKwouXz6WpLR5lZNzN7IaxGepyZrQyrKZ4SLu9sZs+ZWffINo+Z2aDstVpyWasflyiSDHffSjDWHDObDXzm7nOz2ijAg4/zbHz57wzgr+5+GYCZPQNc7u5/TmZfZtbZ99d/yZRLgCfdfa+Z/StBVdL1BG8vXwBcAfyHu38e2eZugppGl2e4rZIHdKcvaWNmZ4R31X8Na4QfkrC8m5k9bWaXm9mh4Tp/CbeZGK4z3cyeNLNnwlriP2vhWBUW1KJ/DTg/Mn+6md1pZmXAz4CJ4V8fswhelrvPzG4L76RvM7MV4Z30v4bbjzOz5WZWBdS0st4y218T/9HwjVLMrNzMXjKzN8Kfr0dL+2nGVPa/fdlAUHWyO9BgZr2BfwIeTthmOfDNSNkCkf2y/faZvgrvC5gN/Jig+t+x4byHCQrMQXCnegzBm4XTwnk3A98Jv+9N8Mb2ocB04D2gF1AMvA/0TzhecXisQQRvKD7O/jd8pwN3Jn4fTi8jfIORoBb5j8PvDyF4O3YgwdvCO4CBSaxXT1D7pBPwMsEvla5h+8vDbXoS/IXd7H4Sfq6uwAeR6aPDNr8MDAN+Doxr4f/B74GR2b4W9JV7X7rTl3QpIiic9U44/RBBzfhG/wU84O6Nd6lnATPNbBVBsBUThBwEHx5R7+47CeqwDEg41uDwWGvd3YH/aEd7zyKoYbKKoNx1H4JfIgB/cfe/J7lerbvvIyixcQxwHLDF3VcAuPt2D7qIDrafRn2BbY0T7r7B3ce5+ynA5wS/YN4ys0cs+DSpYyPbfkhQpVLkAPrzT7LlRaDCzP4zDGoDLnD3t6MrmdnJwK7IrL2k57o14HvuvjTh+OMI7vSTWa8t7Wx2Pwm+IPjl15x/I/hr6hqCD6FZT/DX0tRweXG4vcgBdKcv6bIXOMbMvhZOfxd4IbL8JoKPvbsrnF4KfC/SDz68DcdaEx7rf4bTU9rR3qXAFRaUvsbMjrXgg0zau16jt4Ejzaw8XL9H2Nfe6n7c/ROgyMwOCH4z+0dgs7uvJejf3xd+dY+sdixBwTKRA+hOX9JlJ3Ax8EQYciuA+QnrXAvcHz6cnUXwSVqrzawT8HfgW8kcyN13mlklsNjMPid4kNmjje39FUF3zGvhL546mv9IvmTXa2zbbjObBNxhZt0I7r6/2Yb9PEvwbOA5aCrP/WNgUrh8AfAowb/lK8J1jgC+8KBks8gBVGVTJIeZ2QjgOnf/bhu2uQ7Y7u73pa9lkq/UvSOSw9z9NeB5Mytqw2bb2P8h2iIH0J2+iEiM6E5fRCRGFPoiIjGi0BcRiRGFvohIjCj0RURi5P8DJlrAfHZtoowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(101)\n",
    "plt.hist(100 - all_ratios * 100, bins=bins, histtype=\"step\", density=True, label=\"Positives\", cumulative=True)\n",
    "plt.hist(100 - neg_ratios * 100, bins=bins, histtype=\"step\", density=True, label=\"Negatives\", cumulative=True)\n",
    "plt.xlim([0, 100])\n",
    "plt.ylim([0, 1])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Token difference (%)\")\n",
    "plt.ylabel(\"Cumulative fraction of pairs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean difference, positives: 0.6547359685369606\n",
      "Mean difference, negatives: 0.8626236454234967\n",
      "Median difference, positives: 0.6610169491525424\n",
      "Median difference, negatives: 0.865234375\n",
      "75% difference, positives: 0.7291666666666667\n",
      "75% difference, negatives: 0.9046762589928058\n",
      "25% difference, positives: 0.5869565217391304\n",
      "25% difference, negatives: 0.8217821782178218\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean difference, positives:\", 1 - np.mean(all_ratios))\n",
    "print(\"Mean difference, negatives:\", 1 - np.mean(neg_ratios))\n",
    "print(\"Median difference, positives:\", 1 - np.median(all_ratios))\n",
    "print(\"Median difference, negatives:\", 1 - np.median(neg_ratios))\n",
    "print(\"75% difference, positives:\", np.quantile(1 - all_ratios, 0.75))\n",
    "print(\"75% difference, negatives:\", np.quantile(1 - neg_ratios, 0.75))\n",
    "print(\"25% difference, positives:\", np.quantile(1 - all_ratios, 0.25))\n",
    "print(\"25% difference, negatives:\", np.quantile(1 - neg_ratios, 0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajay/miniconda3/envs/representjs/lib/python3.7/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 5))\n",
    "plt.boxplot([all_ratios, neg_ratios])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41304347826086957"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(all_ratios, 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17094017094017094"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(all_ratios, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22727272727272727"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(neg_ratios, 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09534883720930233"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(neg_ratios, 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC plot of thresholds on ED ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = np.linspace(0, 1, 100)\n",
    "tpr = []\n",
    "fpr = []\n",
    "accs = []\n",
    "for th in thresholds:\n",
    "    fn = np.sum(all_ratios <= th) / len(all_ratios)\n",
    "    tn = np.sum(neg_ratios <= th) / len(neg_ratios)\n",
    "    fp = np.sum(neg_ratios > th) / len(neg_ratios)\n",
    "    tp = np.sum(all_ratios > th) / len(all_ratios)\n",
    "    tpr.append(tp)\n",
    "    fpr.append(fp)\n",
    "\n",
    "#     acc = (np.sum(all_ratios > th) + np.sum(neg_ratios < th)) / (len(all_ratios) + len(neg_ratios))\n",
    "    acc = (np.sqrt(np.sum(all_ratios > th)) + np.sum(neg_ratios < th)) / (np.sqrt(len(all_ratios)) + len(neg_ratios))\n",
    "    accs.append(acc)\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel(\"False positive rate\")\n",
    "plt.ylabel(\"True positive rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa595dcc9d0>]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa4c318ea10>]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(accs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ascii histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ascii_graph\n",
      "  Downloading ascii_graph-1.5.1.tar.gz (24 kB)\n",
      "Building wheels for collected packages: ascii-graph\n",
      "  Building wheel for ascii-graph (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for ascii-graph: filename=ascii_graph-1.5.1-py3-none-any.whl size=11875 sha256=ad4ee08c6cf74dd25826773dd7a27c5adfefbbc3c8b7c55fef64a9a3dcf50dc7\n",
      "  Stored in directory: /home/ajay/.cache/pip/wheels/b1/9a/33/42423a3df123c8d702661601fb0b9e91c68332727dd6dc2125\n",
      "Successfully built ascii-graph\n",
      "Installing collected packages: ascii-graph\n",
      "Successfully installed ascii-graph-1.5.1\n"
     ]
    }
   ],
   "source": [
    "!pip3 install ascii_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test print\n",
      "###############################################################################\n",
      "████████████████████                                            423  long_label\n",
      "█████████████████████████████████████████████████████████████  1234  sl        \n",
      "██████████████████████████                                      531  line3     \n",
      "█████████                                                       200  line4     \n",
      "█████████████████████████████████████████                       834  line5     \n"
     ]
    }
   ],
   "source": [
    "from ascii_graph import Pyasciigraph\n",
    "\n",
    "test = [('long_label', 423), ('sl', 1234), ('line3', 531),\n",
    "    ('line4', 200), ('line5', 834)]\n",
    "\n",
    "graph = Pyasciigraph()\n",
    "for line in  graph.graph('test print', test):\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: https://github.com/Kobold/text_histogram.git is not a valid editable requirement. It should either be a path to a local project or a VCS URL (beginning with svn+, git+, hg+, or bzr+).\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install -e https://github.com/Kobold/text_histogram.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'text_histogram' from '/home/ajay/coderep/representjs/representjs/text_histogram.py'>"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import importlib\n",
    "import text_histogram\n",
    "importlib.reload(text_histogram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# NumSamples = 1644353; Min = 0.00; Max = 100.00\n",
      "# Mean = 65.473597; Variance = 123.592344; SD = 11.117209; Median 66.101695\n",
      "# each ∎ represents a count of 7958\n",
      "   0 -   10 [00%]: \n",
      "  10 -   20 [00%]: \n",
      "  20 -   30 [00%]: \n",
      "  30 -   40 [01%]: ∎∎∎\n",
      "  40 -   50 [07%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  50 -   60 [19%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  60 -   70 [36%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  70 -   80 [26%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  80 -   90 [08%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  90 -  100 [00%]: ∎\n"
     ]
    }
   ],
   "source": [
    "text_histogram.histogram(100 * (1 - all_ratios), buckets=10, minimum=0., maximum=100.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# NumSamples = 1644353; Min = 0.00; Max = 100.00\n",
      "# Mean = 86.262365; Variance = 29.974485; SD = 5.474896; Median 86.523438\n",
      "# each ∎ represents a count of 12798\n",
      "   0 -   10 [00%]: \n",
      "  10 -   20 [00%]: \n",
      "  20 -   30 [00%]: \n",
      "  30 -   40 [00%]: \n",
      "  40 -   50 [00%]: \n",
      "  50 -   60 [00%]: \n",
      "  60 -   70 [00%]: \n",
      "  70 -   80 [13%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  80 -   90 [58%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "  90 -  100 [27%]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n"
     ]
    }
   ],
   "source": [
    "text_histogram.histogram(100 * (1 - neg_ratios), buckets=10, minimum=0., maximum=100.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mtext_histogram\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mminimum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmaximum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbuckets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcustbuckets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcalc_msvd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Loop over the stream and add each entry to the dataset, printing out at the end\n",
       "\n",
       "\n",
       "minimum: minimum value for graph\n",
       "maximum: maximum value for graph\n",
       "buckets: Number of buckets to use for the histogram\n",
       "custbuckets: Comma seperated list of bucket edges for the histogram\n",
       "calc_msvd: Calculate and display Mean, Variance and SD.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/coderep/representjs/representjs/text_histogram.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_histogram.histogram?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# NumSamples = 335360; Min = 0.01; Max = 0.33\n",
      "# Mean = 0.137572; Variance = 0.002969; SD = 0.054486; Median 0.134904\n",
      "# each ∎ represents a count of 924\n",
      "    0.0127 -     0.0448 [ 11146]: ∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.0448 -     0.0768 [ 36771]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.0768 -     0.1089 [ 63286]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.1089 -     0.1410 [ 69370]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.1410 -     0.1730 [ 62669]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.1730 -     0.2051 [ 51936]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.2051 -     0.2371 [ 29229]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎\n",
      "    0.2371 -     0.2692 [  7369]: ∎∎∎∎∎∎∎\n",
      "    0.2692 -     0.3013 [  2624]: ∎∎\n",
      "    0.3013 -     0.3333 [   768]: \n"
     ]
    }
   ],
   "source": [
    "text_histogram.histogram(neg_ratios, buckets=10)"
   ]
  }
 ],
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